Title :
Study on Solution Method for Random Assignment Problem Based on Genetic Algorithm
Author :
Wang, Zhan-jing ; Jin, Chen-xia ; Li, Fa-chao
Author_Institution :
Hebei Univ. of Econ. & Bussiness, Shijiazhuang
Abstract :
In this paper, by introducing the concept of risk critical value of random variable, the risk critical value model based on objective benefit for random assignment problem are proposed. On the basis of characteristic of model, give the concrete implementation strategy and approach based on genetic algorithm (denoted by GARAP, for short), by combining numerical calculation method of probability and evolutionary computation; and consider its convergence using Markov chain theory, and analyze its performance through an example. All these indicate that this model is of strong practicability and good interpretability, and GARAP is of higher computation efficiency and good convergence, can be widely used in decision process.
Keywords :
Markov processes; convergence; decision theory; genetic algorithms; operations research; probability; Markov chain theory convergence; decision process; evolutionary computation; genetic algorithm; numerical calculation method; objective benefit; probability; random assignment problem; risk critical value model; Concrete; Cybernetics; Evolutionary computation; Genetic algorithms; Machine learning; Mathematical model; Mathematics; Probability; Random variables; Uncertainty; Genetic algorithm; Markov chain; Random assignment problem; Random variable; Risk critical value;
Conference_Titel :
Machine Learning and Cybernetics, 2007 International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-0973-0
Electronic_ISBN :
978-1-4244-0973-0
DOI :
10.1109/ICMLC.2007.4370293